Decision tree models, including classifier tree models, are a type of supervised machine learning algorithm that is widely used for both classification and regression tasks. Here's a brief introduction to classifier tree models, covering their purpose, strengths, weaknesses, and the types of data they work best on:
Classifier tree models are used for making decisions based on input features. They recursively split the data into subsets based on the feature values, creating a tree structure where each leaf node corresponds to a class label. During the training process, the algorithm learns a set of decision rules that best separates the classes.
Classifier tree models can work well on various types of data, including:
In practice, decision trees are often used as building blocks for more sophisticated models, such as random forests or gradient boosting, which address some of the weaknesses associated with individual decision trees.
Snap ML is a machine learning library developed by IBM for use with Apache Spark. It is designed to accelerate and scale machine learning workflows on Spark, allowing data scientists and developers to train and deploy machine learning models efficiently.
Here are some key features and aspects of Snap ML:
Performance Optimization: Snap ML is designed to enhance the performance of machine learning tasks on large-scale distributed computing frameworks, such as Apache Spark. It leverages hardware acceleration, including GPUs, to speed up training and inference processes.
Compatibility with Spark: Snap ML integrates seamlessly with Apache Spark, making it easier to incorporate machine learning into Spark-based big data processing pipelines. This compatibility allows users to take advantage of Spark's distributed computing capabilities.
Support for Various Machine Learning Algorithms: Snap ML provides a set of machine learning algorithms that cover tasks such as classification, regression, and clustering. These algorithms are optimized for performance and scalability.
Distributed Training: The library supports distributed training of machine learning models, enabling the processing of large datasets across multiple nodes in a Spark cluster.
Scalability: Snap ML is designed to scale horizontally, making it suitable for handling large datasets and training complex models in a distributed computing environment.
# Snap ML is available on PyPI. To install it simply run the pip command below.
# !pip install snapml
# %%bash
# missing library was throwing an error during import
# brew install libomp
# install the opendatasets package
!pip install opendatasets
import opendatasets as od
# download the dataset (this is a Kaggle dataset)
# during download you will be required to input your Kaggle username and password
od.download("https://www.kaggle.com/mlg-ulb/creditcardfraud")
# Use URL if Kaggle fails...
# url= "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/creditcard.csv"
# raw_data=pd.read_csv(url)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from sklearn.preprocessing import StandardScaler, normalize
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.utils import compute_sample_weight
from sklearn.metrics import roc_auc_score
import time
import snapml
%matplotlib inline
# read the input data
raw_data = pd.read_csv('creditcardfraud/creditcard.csv')
print("There are " + str(len(raw_data)) + " observations in the credit card fraud dataset.")
print("There are " + str(len(raw_data.columns)) + " variables in the dataset.")
print("There are " + str(len(raw_data.Time.unique())) + " unique elements in the Time column.")
# display the first rows in the dataset
raw_data.head()
There are 284807 observations in the credit card fraud dataset. There are 31 variables in the dataset. There are 124592 unique elements in the Time column.
| Time | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | ... | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | -1.359807 | -0.072781 | 2.536347 | 1.378155 | -0.338321 | 0.462388 | 0.239599 | 0.098698 | 0.363787 | ... | -0.018307 | 0.277838 | -0.110474 | 0.066928 | 0.128539 | -0.189115 | 0.133558 | -0.021053 | 149.62 | 0 |
| 1 | 0.0 | 1.191857 | 0.266151 | 0.166480 | 0.448154 | 0.060018 | -0.082361 | -0.078803 | 0.085102 | -0.255425 | ... | -0.225775 | -0.638672 | 0.101288 | -0.339846 | 0.167170 | 0.125895 | -0.008983 | 0.014724 | 2.69 | 0 |
| 2 | 1.0 | -1.358354 | -1.340163 | 1.773209 | 0.379780 | -0.503198 | 1.800499 | 0.791461 | 0.247676 | -1.514654 | ... | 0.247998 | 0.771679 | 0.909412 | -0.689281 | -0.327642 | -0.139097 | -0.055353 | -0.059752 | 378.66 | 0 |
| 3 | 1.0 | -0.966272 | -0.185226 | 1.792993 | -0.863291 | -0.010309 | 1.247203 | 0.237609 | 0.377436 | -1.387024 | ... | -0.108300 | 0.005274 | -0.190321 | -1.175575 | 0.647376 | -0.221929 | 0.062723 | 0.061458 | 123.50 | 0 |
| 4 | 2.0 | -1.158233 | 0.877737 | 1.548718 | 0.403034 | -0.407193 | 0.095921 | 0.592941 | -0.270533 | 0.817739 | ... | -0.009431 | 0.798278 | -0.137458 | 0.141267 | -0.206010 | 0.502292 | 0.219422 | 0.215153 | 69.99 | 0 |
5 rows × 31 columns
In the context of Classification Tree Analysis, when you mention "replicas," it's possible that you are referring to the concept of replication in the context of statistical or machine learning experiments. Replication involves creating multiple copies or instances of a dataset to assess the stability and reliability of a model's performance. Below are some reasons why replicas might be used in the context of Classification Tree Analysis:
Bootstrap Aggregating (Bagging): Replicas are often used in techniques like Bootstrap Aggregating or Bagging. In Bagging, multiple bootstrap samples (replicas) are created by randomly drawing samples with replacement from the original dataset. A classification tree is then trained on each bootstrap sample, and the final prediction is obtained by averaging (for regression) or voting (for classification) across all trees. This helps to reduce overfitting and improve the stability of the model.
Random Forests: Random Forests, an ensemble method based on Bagging, extends the concept by introducing additional randomness. In addition to creating replicas through bootstrap sampling, Random Forests also randomly select a subset of features for each split in the tree-building process. This further enhances the diversity of the individual trees and improves the overall model's generalization.
Cross-Validation: Replicas can be used in the context of cross-validation. In k-fold cross-validation, the dataset is divided into k subsets, and the model is trained and evaluated k times, each time using a different subset as the test set. This process is repeated to create multiple replicas of the cross-validation procedure, providing a more robust estimate of the model's performance.
Assessing Variability: Replicas help in assessing the variability or uncertainty associated with model predictions. By training and evaluating the model on multiple replicas, you can get a sense of how stable the model is under different subsamples of the data. This information is valuable for understanding the reliability of the model's predictions.
Hyperparameter Tuning: Replicas may also be used in hyperparameter tuning processes, where different configurations of hyperparameters are evaluated on multiple replicas of the training data. This helps in finding hyperparameter settings that generalize well across different subsets of the data.
In summary, the use of replicas in Classification Tree Analysis, especially in ensemble methods like Bagging and Random Forests, aims to improve model stability, reduce overfitting, and provide a more reliable estimate of the model's performance on unseen data.
n_replicas = 10
# inflate the original dataset for the sake of having a larger dataset
big_raw_data = pd.DataFrame(np.repeat(raw_data.values, n_replicas, axis=0), columns=raw_data.columns)
print("There are " + str(len(big_raw_data)) + " observations in the inflated credit card fraud dataset.")
print("There are " + str(len(big_raw_data.columns)) + " variables in the dataset.")
# display first rows in the new dataset
big_raw_data.head()
There are 2848070 observations in the inflated credit card fraud dataset. There are 31 variables in the dataset.
| Time | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | ... | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | -1.359807 | -0.072781 | 2.536347 | 1.378155 | -0.338321 | 0.462388 | 0.239599 | 0.098698 | 0.363787 | ... | -0.018307 | 0.277838 | -0.110474 | 0.066928 | 0.128539 | -0.189115 | 0.133558 | -0.021053 | 149.62 | 0.0 |
| 1 | 0.0 | -1.359807 | -0.072781 | 2.536347 | 1.378155 | -0.338321 | 0.462388 | 0.239599 | 0.098698 | 0.363787 | ... | -0.018307 | 0.277838 | -0.110474 | 0.066928 | 0.128539 | -0.189115 | 0.133558 | -0.021053 | 149.62 | 0.0 |
| 2 | 0.0 | -1.359807 | -0.072781 | 2.536347 | 1.378155 | -0.338321 | 0.462388 | 0.239599 | 0.098698 | 0.363787 | ... | -0.018307 | 0.277838 | -0.110474 | 0.066928 | 0.128539 | -0.189115 | 0.133558 | -0.021053 | 149.62 | 0.0 |
| 3 | 0.0 | -1.359807 | -0.072781 | 2.536347 | 1.378155 | -0.338321 | 0.462388 | 0.239599 | 0.098698 | 0.363787 | ... | -0.018307 | 0.277838 | -0.110474 | 0.066928 | 0.128539 | -0.189115 | 0.133558 | -0.021053 | 149.62 | 0.0 |
| 4 | 0.0 | -1.359807 | -0.072781 | 2.536347 | 1.378155 | -0.338321 | 0.462388 | 0.239599 | 0.098698 | 0.363787 | ... | -0.018307 | 0.277838 | -0.110474 | 0.066928 | 0.128539 | -0.189115 | 0.133558 | -0.021053 | 149.62 | 0.0 |
5 rows × 31 columns
# get the set of distinct classes
labels = big_raw_data.Class.unique()
# get the count of each class
sizes = big_raw_data.Class.value_counts().values
# plot the class value counts
fig, ax = plt.subplots()
ax.pie(sizes, labels=labels, autopct='%1.3f%%')
ax.set_title('Target Variable Value Counts')
plt.show()
As shown above, the Class variable has two values: 0 (the credit card transaction is legitimate) and 1 (the credit card transaction is fraudulent). Thus, you need to model a binary classification problem. Moreover, the dataset is highly unbalanced, the target variable classes are not represented equally. This case requires special attention when training or when evaluating the quality of a model. One way of handing this case at train time is to bias the model to pay more attention to the samples in the minority class. The models under the current study will be configured to take into account the class weights of the samples at train/fit time.
print("Minimum amount value is ", np.min(big_raw_data.Amount.values))
print("Maximum amount value is ", np.max(big_raw_data.Amount.values))
print("90% of the transactions have an amount less or equal than ", np.percentile(raw_data.Amount.values, 90))
flat_data = big_raw_data.Amount.values.flatten()
# Create a histogram
fig = go.Figure(data=[go.Histogram(x=flat_data, nbinsx=50)])
# Update layout
fig.update_layout(
title='Histogram of All Values Across All Columns',
xaxis_title='Values',
yaxis_title='Frequency'
)
# Show the plot
fig.show()
Minimum amount value is 0.0 Maximum amount value is 25691.16 90% of the transactions have an amount less or equal than 203.0